Ignore:
Timestamp:
02/27/13 15:02:50 (14 months ago)
Author:
Ales Erjavec <ales.erjavec@…>
Branch:
default
Message:

Cleanup of 'Widget catalog' documentation.

Fixed rst text formating, replaced dead hardcoded reference links (now using
:ref:), etc.

File:
1 edited

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  • docs/widgets/rst/classify/naivebayes.rst

    r11050 r11359  
    2121 
    2222   - Learner 
    23       The naive Bayesian learning algorithm with settings as specified in the dialog. 
     23      The naive Bayesian learning algorithm with settings as specified in 
     24      the dialog. 
    2425 
    2526   - Naive Bayesian Classifier 
     
    2728 
    2829 
    29 Signal :code:`Naive Bayesian Classifier` sends data only if the learning data (signal :code:`Examples` is present. 
     30Signal :code:`Naive Bayesian Classifier` sends data only if the learning 
     31data (signal :code:`Examples` is present. 
    3032 
    3133Description 
     
    3436This widget provides a graphical interface to the Naive Bayesian classifier. 
    3537 
    36 As all widgets for classification, this widget provides a learner and classifier on the output. Learner is a learning algorithm with settings as specified by the user. It can be fed into widgets for testing learners, for instance :code:`Test Learners`. Classifier is a Naive Bayesian Classifier (a subtype of a general classifier), built from the training examples on the input. If examples are not given, there is no classifier on the output. 
     38As all widgets for classification, this widget provides a learner and 
     39classifier on the output. Learner is a learning algorithm with settings 
     40as specified by the user. It can be fed into widgets for testing learners, 
     41for instance :ref:`Test Learners`. Classifier is a Naive Bayesian Classifier 
     42(a subtype of a general classifier), built from the training examples on the 
     43input. If examples are not given, there is no classifier on the output. 
    3744 
    3845.. image:: images/NaiveBayes.png 
    3946   :alt: NaiveBayes Widget 
    4047 
    41 Learner can be given a name under which it will appear in, say, :code:`Test Learners`. The default name is "Naive Bayes". 
     48Learner can be given a name under which it will appear in, say, 
     49:ref:`Test Learners`. The default name is "Naive Bayes". 
    4250 
    43 Next come the probability estimators. :obj:`Prior` sets the method used for estimating prior class probabilities from the data. You can use either :obj:`Relative frequency` or the :obj:`Laplace estimate`. :obj:`Conditional (for discrete)` sets the method for estimating conditional probabilities, besides the above two, conditional probabilities can be estimated using the :obj:`m-estimate`; in this case the value of m should be given as the :obj:`Parameter for m-estimate`. By setting it to :obj:`&lt;same as above&gt;` the classifier will use the same method as for estimating prior probabilities. 
     51Next come the probability estimators. :obj:`Prior` sets the method used for 
     52estimating prior class probabilities from the data. You can use either 
     53:obj:`Relative frequency` or the :obj:`Laplace estimate`. 
     54:obj:`Conditional (for discrete)` sets the method for estimating conditional 
     55probabilities, besides the above two, conditional probabilities can be 
     56estimated using the :obj:`m-estimate`; in this case the value of m should be 
     57given as the :obj:`Parameter for m-estimate`. By setting it to 
     58:obj:`<same as above>` the classifier will use the same method as for 
     59estimating prior probabilities. 
    4460 
    45 Conditional probabilities for continuous attributes are estimated using LOESS. :obj:`Size of LOESS window` sets the proportion of points in the window; higher numbers mean more smoothing. :obj:`LOESS sample points` sets the number of points in which the function is sampled. 
     61Conditional probabilities for continuous attributes are estimated using 
     62LOESS. :obj:`Size of LOESS window` sets the proportion of points in the 
     63window; higher numbers mean more smoothing. 
     64:obj:`LOESS sample points` sets the number of points in which the function 
     65is sampled. 
    4666 
    47 If the class is binary, the classification accuracy may be increased considerably by letting the learner find the optimal classification threshold (option :obj:`Adjust threshold`). The threshold is computed from the training data. If left unchecked, the usual threshold of 0.5 is used. 
     67If the class is binary, the classification accuracy may be increased 
     68considerably by letting the learner find the optimal classification 
     69threshold (option :obj:`Adjust threshold`). The threshold is computed from 
     70the training data. If left unchecked, the usual threshold of 0.5 is used. 
    4871 
    49 When you change one or more settings, you need to push :obj:`Apply`; this will put the new learner on the output and, if the training examples are given, construct a new classifier and output it as well. 
     72When you change one or more settings, you need to push :obj:`Apply`; 
     73this will put the new learner on the output and, if the training examples 
     74are given, construct a new classifier and output it as well. 
    5075 
    5176 
     
    5378-------- 
    5479 
    55 There are two typical uses of this widget. First, you may want to induce the model and check what it looks like in a `Nomogram <Nomogram.htm>`_. 
     80There are two typical uses of this widget. First, you may want to induce 
     81the model and check what it looks like in a :ref:`Nomogram`. 
    5682 
    5783.. image:: images/NaiveBayes-SchemaClassifier.png 
    5884   :alt: Naive Bayesian Classifier - Schema with a Classifier 
    5985 
    60 The second schema compares the results of Naive Bayesian learner with another learner, a C4.5 tree. 
     86The second schema compares the results of Naive Bayesian learner with 
     87another learner, a C4.5 tree. 
    6188 
    6289.. image:: images/C4.5-SchemaLearner.png 
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